Is Social Sciences and Humanities (SSH) Premedical Education Marginalized in the Medical School Admission Process? A Review and Contextualization of the Literature
Bibliographic record
Abstract
PURPOSE: To investigate the performance outcomes of medical students with social sciences and humanities (SSH) premedical education during and beyond medical school by reviewing the literature, and to contextualize this review within today's admission milieu. METHOD: From May to July 2012, the lead author searched the PubMed, MEDLINE, and PsycINFO databases, and reference lists of relevant articles, for research that compared premedical SSH education with premedical sciences education and its influence on performance during and/or after medical school. The authors extracted representative themes and relevant empirical findings. They contextualized their findings within today's admission milieu. RESULTS: A total of 1,548 citations were identified with 20 papers included in the review. SSH premedical education is predominately an American experience. For medical students with SSH background, equivalent academic, clinical, and research performance compared with medical students with a premedical science background is reported, yet different patterns of competencies exist. Post-medical-school equivalent or improved clinical performance is associated with an SSH background. Medical students with SSH backgrounds were more likely to select primary care or psychiatry careers. SSH major/course concentration, not SSH course counts, is important for admission decision making. The impact of today's admission milieu decreases the value of an SSH premedical education. CONCLUSIONS: Medical students with SSH premedical education perform on par with peers yet may possess different patterns of competencies, research, and career interests. However, SSH premedical education likely will not attain a significant role in medical school admission processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".